In this study, we propose a method for learning interactions among different types of objects to devise new plans using these objects. Learning is accomplished by observing a given sequence of events with their timestamps and using spatial information on the initial state of the objects in the environment. We assume that no intermediate state information is available about the states of objects. We have used the Incredible Machine game as a suitable domain for analyzing and learning object interactions. When a knowledge base about relations among objects is provided, interactions to devise new plans are learned to a desired extent. Moreover, using spatial information of objects or temporal information of events makes it feasible to learn the conditional effects of objects on each other. Our analyses show that, integrating spatial and temporal data in a spatio-temporal learning approach gives closer results to that of the knowledge-based approach by providing applicable event models for planning. This is promising because gathering spatio-temporal information does not require great amount of knowledge.

We present a study of how humans represent space when solving Tower Defense puzzles, a complex spatial reasoning task requiring the subject to protect locations by arranging a set of defense towers at strategic positions. We have discovered that the representation humans use is significantly more complex than what is needed to describe the spatial situation. Strategy and spatial representations are tightly intertwined with spatial representations forgoing objective, atomically-defined spatial features for context-sensitive, goal-oriented spatial affordances. Spatial relationships exist not only between objects but between an object’s properties, second-order properties, joint spatial properties and temporal properties.

In order to collaborate with people in the real world, cognitive systems must be able to represent and reason about spatial regions in human environments. Consider the command "go to the front of the classroom". The spatial region mentioned (the front of the classroom) is not perceivable using geometry alone. Instead it is defined by its functional use, implied by nearby objects and their configuration. In this paper, we define such areas as context-dependent spatial regions and present a cognitive system able to learn them by combining qualitative spatial representations, semantic labels, and analogy. The system is capable of generating a collection of qualitative spatial representations describing the configuration of the entities it perceives in the world. It can then be taught context-dependent spatial regions using anchor pointsdefined on these representations. From this we then demonstrate how an existing computational model of analogy can be used to detect context-dependent spatial regions in previously unseen rooms. To evaluate this process we compare detected regions to annotations made on maps of real rooms by human volunteers.

We consider the problem of learning object arrangements in a 3D scene. The key idea here is to learn how objects relate to human poses based on their affordances, ease of use and reachability. In contrast to modeling object-object relationships, modeling human-object relationships scales linearly in the number of objects. We design appropriate density functions based on 3D spatial features to capture this. We learn the distribution of human poses in a scene using a variant of the Dirichlet process mixture model that allows sharing of the density function parameters across the same object types. Then we can reason about arrangements of the objects in the room based on these meaningful human poses. In our extensive experiments on 20 different rooms with a total of 47 objects, our algorithm predicted correct placements with an average error of 1.6 meters from ground truth. In arranging five real scenes, it received a score of 4.3/5 compared to 3.7 for the best baseline method.

In this paper, we investigate the application of data mining to existing techniques for quality control/anomaly detection on weather sensor observations. Specifically we adapt the popular Barnes Spatial interpolation method to use time-series distance rather than spatial distance to develop an online algorithm that uses readings from similar stations based on current and historical observations for interpolation and we demonstrate that this new algorithm exhibits less model error than the Barnes Spatial interpolation-based method. We focus on interpolation, which is a basis for this popular quality control method and other related methods, and examine a dataset of over 233 million temperature observations from California and surrounding areas. Our approach shows improved performance as indicated by mean squared error reduced by approximately one half for predicted values versus reported values.

We introduce a challenging real-world planning problem where actions must be taken at each location in a spatial area at each point in time. We use forestry planning as the motivating application. In Large Scale Spatial-Temporal (LSST) planning problems, the state and action spaces are defined as the cross-products of many local state and action spaces spread over a large spatial area such as a city or forest. These problems possess state uncertainty, have complex utility functions involving spatial constraints and we generally must rely on simulations rather than an explicit transition model. We define LSST problems as reinforcement learning problems and present a solution using policy gradients. We compare two different policy formulations: an explicit policy that identifies each location in space and the action to take there; and an abstract policy that defines the proportion of actions to take across all locations in space. We show that the abstract policy is more robust and achieves higher rewards with far fewer parameters than the elementary policy. This abstract policy is also a better fit to the properties that practitioners in LSST problem domains require for such methods to be widely useful.

Recent research suggests that, as in offline scenarios, spatial proximity increases the likelihood that two individuals establish an online social connection, and geographic closeness could therefore influence the formation of online communities. In this work we present a study of communities in two online social networks with location-sharing features and analyze their social and spatial properties. We study the places users visit to understand whether communities revolve around places or whether they exist independently. Our results suggest that community structure in social networks may arise from both social and spatial factors, so that exploiting information about the places where people go could benefit the definition of new community detection methods and community evolution models.

The popularity of the Web has allowed individuals to communicate and interact with each other on a global scale: people connect both to close friends and acquaintances, creating ties that can bridge otherwise separated groups of people. Recent evidence suggests that spatial distance is still affecting social links established on online platforms, with online ties preferentially connecting closer people. In this work we study the relationships between interaction strength, spatial distance and structural position of ties between members of a large-scale online social networking platform, Tuenti. We discover that ties in highly connected social groups tend to span shorter distances than connections bridging together otherwise separated portions of the network. We also find that such bridging connections have lower social interaction levels than ties within the inner core of the network and ties connecting to its periphery. Our results suggest that spatial constraints on online social networks are intimately connected to structural network properties, with important consequences for information diffusion.

Location-based social networks (LBSNs) have become a popular form of social media in recent years. They provide location related services that allow users to "check-in'' at geographical locations and share such experiences with their friends. Millions of "check-in'' records in LBSNs contain rich information of social and geographical context and provide a unique opportunity for researchers to study user's social behavior from a spatial-temporal aspect, which in turn enables a variety of services including place advertisement, traffic forecasting, and disaster relief. In this paper, we propose a social-historical model to explore user's check-in behavior on LBSNs. Our model integrates the social and historical effects and assesses the role of social correlation in user's check-in behavior. In particular, our model captures the property of user's check-in history in forms of power-law distribution and short-term effect, and helps in explaining user's check-in behavior. The experimental results on a real world LBSN demonstrate that our approach properly models user's check-ins and shows how social and historical ties can help location prediction.

Successful analysis of video data requires an integration of techniques from KR, Computer Vision, and Machine Learning. Being able to detect and to track objects as well as extracting their changing spatial relations with other objects is one approach to describing and detecting events. Different kinds of spatial relations are important, including topology, direction, size, and distance between objects as well as changes of those relations over time. Typically these kinds of relations are treated separately, which makes it difficult to integrate all the extracted spatial information. We present a uniform and comprehensive spatial representation of moving objects that includes all the above spatial/temporal aspects, analyse different properties of this representation and demonstrate that it is suitable for video analysis.